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Codebook

About: Codebook is a research topic. Over the lifetime, 8492 publications have been published within this topic receiving 115995 citations.


Papers
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Patent
27 Jan 1998
TL;DR: In this paper, each speech frame is represented by a weighted average of codebook entries and the weights represent a perceptual distance of the speech frame and may be refined by a gradient descent analysis.
Abstract: A voice conversion system and methodology employing a codebook mapping approach to transforming a source voice to sound like a target voice. Each speech frame is represented by a weighted average of codebook entries (304). The weights represent a perceptual distance of the speech frame and may be refined by a gradient descent analysis. The vocal tract characteristics, represented by a line spectral frequency vector (302), the excitation characteristics (308), represented by a linear predictive coding residual, the duration, and the amplitude of the speech frame are transformed in the same weighted-average framework.

62 citations

Journal ArticleDOI
TL;DR: An architecture suitable for real-time image coding using adaptive vector quantization (VQ) is presented, where the data is accessed simultaneously and in parallel on the basis of its content.
Abstract: An architecture suitable for real-time image coding using adaptive vector quantization (VQ) is presented. This architecture is based on the concept of content-addressable memory (CAM), where the data is accessed simultaneously and in parallel on the basis of its content. VQ essentially involves, for each input vector, a search operation to obtain the best match codeword. A speedup results if a CAM-based implementation is used. This speedup, coupled with the gains in execution time for the basic distortion operation, implies that even codebook generation is possible in real time ( >

61 citations

Patent
14 Jul 2006
TL;DR: In this article, various optional features are described for modifying the code-vectors in the codebook according to some rules which allow the codevector to better represent the data they are modeling.
Abstract: Coding of spectral data by representing certain portions of the spectral data as a scaled version of a code-vector, where the code-vector is chosen from either a fixed predetermined codebook or a codebook taken from a baseband. Various optional features are described for modifying the code-vectors in the codebook according to some rules which allow the code-vector to better represent the data they are modeling. The code-vector modification comprises a linear or non-linear transform of one or more code-vectors, such as, by exponentiation, negation, reversing, or combining elements from plural code-vectors.

61 citations

Journal ArticleDOI
TL;DR: This paper investigates how angle-of-arrival (AoA) information can be exploited by deep-/machine-learning approaches to perform beam selection in the uplink of a mmWave communication system and reveals that performance is very close to that achievable via exhaustive search.
Abstract: This paper investigates how angle-of-arrival (AoA) information can be exploited by deep-/machine-learning approaches to perform beam selection in the uplink of a mmWave communication system. Specifically, we consider a hybrid beamforming setup comprising an analog beamforming (ABF) network with adjustable beamwidth followed by a zero-forcing baseband processing block. The goal is to select the optimal configuration for the ABF network based on the estimated AoAs of the various user equipments. To that aim, we consider 1) two supervised machine-learning approaches: k -nearest neighbors (kNN) and support vector classifiers (SVC); and 2) a feed-forward deep neural network: the multilayer perceptron. We conduct an extensive performance evaluation to investigate the impact of the quality of CSI estimates (AoAs and powers) obtained via the Capon or MUSIC methods, fluctuations in the received power, the size of the training dataset, the total number of analog beamformers in the codebook, their beamwidth, or the number of active users. The computer simulations reveal that performance, in terms of classification accuracy and sum-rate is very close to that achievable via exhaustive search.

61 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully connected structure, under a more realistic hardware network model, particularly, with inevitable dissipation.
Abstract: In this paper, we study the hybrid precoding structures over limited feedback channels for massive multiuser multiple-input multiple-output (MIMO) systems. We focus on the system performance of hybrid precoding under a more realistic hardware network model, particularly, with inevitable dissipation. The effect of quantized analog and digital precoding is characterized. We investigate the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully connected structure. It is revealed that increasing signal power can compensate for the performance loss incurred by quantized analog precoding. In addition, by capturing the nature of the effective channels for hybrid processing, we employ a channel correlation-based codebook and demonstrate that the codebook shows a great advantage over the conventional random vector quantization codebook. It is also discovered that, if the channel correlation-based codebook is utilized, the sub-connected structure always outperforms the fully connected structure in either massive MIMO or low signal-to-noise ratio scenarios; otherwise, the fully-connected structrue achieves better performance. Simulation results under both Rayleigh fading channels and millimeter wave (mm-wave) channels verify the conclusions above.

61 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023217
2022495
2021237
2020383
2019432
2018364